import pandas as pd
import bokeh
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
from bokeh.models import LinearColorMapper
output_notebook()
import numpy as np
SBOX = [
# 0 1 2 3 4 5 6 7 8 9 a b c d e f
0x63,0x7c,0x77,0x7b,0xf2,0x6b,0x6f,0xc5,0x30,0x01,0x67,0x2b,0xfe,0xd7,0xab,0x76, # 0
0xca,0x82,0xc9,0x7d,0xfa,0x59,0x47,0xf0,0xad,0xd4,0xa2,0xaf,0x9c,0xa4,0x72,0xc0, # 1
0xb7,0xfd,0x93,0x26,0x36,0x3f,0xf7,0xcc,0x34,0xa5,0xe5,0xf1,0x71,0xd8,0x31,0x15, # 2
0x04,0xc7,0x23,0xc3,0x18,0x96,0x05,0x9a,0x07,0x12,0x80,0xe2,0xeb,0x27,0xb2,0x75, # 3
0x09,0x83,0x2c,0x1a,0x1b,0x6e,0x5a,0xa0,0x52,0x3b,0xd6,0xb3,0x29,0xe3,0x2f,0x84, # 4
0x53,0xd1,0x00,0xed,0x20,0xfc,0xb1,0x5b,0x6a,0xcb,0xbe,0x39,0x4a,0x4c,0x58,0xcf, # 5
0xd0,0xef,0xaa,0xfb,0x43,0x4d,0x33,0x85,0x45,0xf9,0x02,0x7f,0x50,0x3c,0x9f,0xa8, # 6
0x51,0xa3,0x40,0x8f,0x92,0x9d,0x38,0xf5,0xbc,0xb6,0xda,0x21,0x10,0xff,0xf3,0xd2, # 7
0xcd,0x0c,0x13,0xec,0x5f,0x97,0x44,0x17,0xc4,0xa7,0x7e,0x3d,0x64,0x5d,0x19,0x73, # 8
0x60,0x81,0x4f,0xdc,0x22,0x2a,0x90,0x88,0x46,0xee,0xb8,0x14,0xde,0x5e,0x0b,0xdb, # 9
0xe0,0x32,0x3a,0x0a,0x49,0x06,0x24,0x5c,0xc2,0xd3,0xac,0x62,0x91,0x95,0xe4,0x79, # a
0xe7,0xc8,0x37,0x6d,0x8d,0xd5,0x4e,0xa9,0x6c,0x56,0xf4,0xea,0x65,0x7a,0xae,0x08, # b
0xba,0x78,0x25,0x2e,0x1c,0xa6,0xb4,0xc6,0xe8,0xdd,0x74,0x1f,0x4b,0xbd,0x8b,0x8a, # c
0x70,0x3e,0xb5,0x66,0x48,0x03,0xf6,0x0e,0x61,0x35,0x57,0xb9,0x86,0xc1,0x1d,0x9e, # d
0xe1,0xf8,0x98,0x11,0x69,0xd9,0x8e,0x94,0x9b,0x1e,0x87,0xe9,0xce,0x55,0x28,0xdf, # e
0x8c,0xa1,0x89,0x0d,0xbf,0xe6,0x42,0x68,0x41,0x99,0x2d,0x0f,0xb0,0x54,0xbb,0x16 # f
]
def sbox(data):
return SBOX[data]
sbox_vec = np.vectorize(sbox)
import numpy as np
HW = [bin(n).count("1") for n in range(0, 256)]
def hw(n):
if isinstance(n, str):
return HW[ord(n)]
return HW[n]
hw_vec = np.vectorize(hw)
def pearson(x, y):
x_mean = np.mean(x)
y_mean = np.mean(y)
return sum((x - x_mean) * (y - y_mean)) / np.sqrt(sum((x - x_mean) ** 2) * sum((y - y_mean) ** 2))
def pearson_pointwise(traces, intermediates):
intermediates_diff = intermediates - np.mean(intermediates)
intermediates_sqrt = np.sqrt(np.sum(intermediates_diff ** 2))
traces_diff = traces - np.mean(traces, axis=0)
return np.sum(traces_diff * intermediates_diff[:, None], axis=0) / (
np.sqrt(np.sum(traces_diff ** 2, axis=0)) * intermediates_sqrt
)
import sys
sys.path.append('..')
import securec
from securec import util
scope, target = util.init()
WARNING:ChipWhisperer Other:ChipWhisperer update available! See https://chipwhisperer.readthedocs.io/en/latest/installing.html for updating instructions
securec.util.compile_and_flash('./5_aes_fixed.c', cryptooptions=['CRYPTO_TARGET=TINYAES128C'])
XMEGA Programming flash...
XMEGA Reading flash...
Verified flash OK, 3901 bytes
✓
import numpy as np
scope.default_setup()
def capture(pt, samples=500):
scope.adc.samples = samples
scope.arm()
target.simpleserial_write(0x01, pt)
return np.array(util.capture())
import random
import tqdm
import tqdm.notebook
trace_samples = 4000
trace_nums = 500
traces = []
inputs = []
for _ in tqdm.notebook.tqdm(range(trace_nums)):
input = bytes([random.randint(0, 255) for _ in range(16)])
traces.append(capture(input, samples=trace_samples))
inputs.append(input)
traces = np.array(traces)
attempts = np.array([list(a) for a in inputs])
colormap = LinearColorMapper(
palette='Viridis256',
low=0,
high=1,
)
def correlation_plot(key_idx, chars=range(256), trace_range=(0, 4000)):
traces_cropped = traces[:, trace_range[0]:trace_range[1]]
pearsons = [abs(pearson_pointwise(traces_cropped, hw_vec(sbox_vec(attempts[:, key_idx] ^ i)))) for i in chars]
df = pd.DataFrame(pearsons, index=chars)
df = df.stack().reset_index()
df.columns=['char', 'point', 'value']
p = figure(
height=1000,
sizing_mode='stretch_width',
tooltips=[
("char", "@char"),
("corr", "@value"),
],
title=f'Correlations for guessing position {key_idx}'
)
p.rect(
width=1,
height=1,
source=df,
x='point',
y='char',
fill_color={'field': 'value', 'transform': colormap},
line_color=None,
)
show(p)
correlation_plot(0)